Integrity constraints in graph databases - implementation challenges

Graph databases are becoming more and more popular as they represent a good alternative to relational databases for some problem scenarios. Searching a graph is sometimes very convenient, unlike writing complex SQL queries that require a table to be joined to itself several times. However, graph databases do not support all the constraints that are familiar and used in relational databases. In this paper, we discuss integrity constraints in graph databases and technical implementation issues that prevent these constraints from being specified

Integrity constraints in graph databases - implementation challenges

Graph databases are becoming more and more popular as they represent a good alternative to relational databases for some problem scenarios. Searching a graph is sometimes very convenient, unlike writing complex SQL queries that require a table to be joined to itself several times. However, graph databases do not support all the constraints that are familiar and used in relational databases. In this paper, we discuss integrity constraints in graph databases and technical implementation issues that prevent these constraints from being specified

Graph Databases - are they really so new

Even though relational databases have been (and still are) the most widely used database solutions for many years, there were other database solutions in use before the era of relational databases. One of those solutions were network databases with the underlying network model, whose characteristics will be presented in detail in this paper. The network model will be compared to the graph data model used by graph databases, the relatively new category of NoSQL databases with a growing share in the database market. The similarities and differences will be shown through the implementation of a simple database using network and graph data model

Exploring graph databases

Kako smo unatrag nekoliko godina dobili određena sredstva (tzv. sveučilišne potpore) kojima smo istraživali određene aspekte grafovskih baza podataka, odlučili smo rezultate istraživanja predstaviti i hrvatskoj akademskoj i stručnoj zajednici. Smatramo kako su istraživanja bila kvalitetna i da rezultati istih imaju potencijal za širu primjenu i implementaciju u dostupnim sustavima za upravljanje grafovskim bazama podataka. Također, postoje i određene mogućnosti za proširenje implementiranih rješenja, kao i prostor za dodatna istraživanja i preslikavanja relacijskih u koncepte grafovskih baza podataka.We received certain funds (so-called University grants) a few years ago to explore certain aspects of graph databases. In this study, we decided to share the results of the research to the Croatian academic and professional community. We believe that the research was of high quality and that the results have the potential for wider application and implementation in the available graph database management systems. In addition, there are certain possibilities for expanding the implemented solutions, and there is space for additional research and mapping of relational into graph database concepts

Relaatiotietokannasta graafitietokantaan : graafitietokannan edut tietojärjestelmän tietovarastona

Relaatiotietomalli on vakiintunut tapa organisoida ja käsitellä tietojärjestelmän tietoja. Sosiaalisen median kasvun, sekä uudenlaisen sisällön tuottamiseen keskittyneen internetkulttuurin myötä uudet, erilaiset tavat mallintaa tietoa ovat nousseet esiin. Vaatimusten kasvaessa tiedon nopean saatavuuden suhteen, ovat NoSQL-pohjaiset tietovarastot tulleet usein osaksi tietojärjestelmän kokonaisarkkitehtuuria. Tässä tutkielmassa esitellään tietomalleja, jotka ovat tietojärjestelmäkehityksessä relaatiotietomallin ohella keskeisiä tapoja mallintaa tietoa. Syvällisemmin keskitytään relaatiotietokannan ja graafitietokannan eroihin. Tutkielmassa etsitään myös tapauksia, joissa perinteisen relaatiopohjaisen tietokannan muuttaminen graafipohjaiseksi tuo esiin konkreettisia hyötyjä. Nämä hyödyt saattavat olla esimerkiksi tiedonmallinnukseen liittyviä, semanttisia tai arkkitehtuurisia hyötyjä. Hyödyt saattavat olla myös merkittäviä tiedonhaun kannalta, esimerkiksi yksinkertaisempien ja tehokkaampien hakukyselyjen muodossa. Toisaalta tutkielmassa huomioidaan tapaukset, joissa graafitietokantaan siirtymiselle ei löydy perusteltua syytä

Aprendizado de representações e correspondências baseadas em grafos para tarefas de classificação

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Muitas situações do mundo real podem ser modeladas por meio de objetos e seus relacionamentos, como, por exemplo, estradas conectando cidades em um mapa. Grafo é um conceito derivado da abstração dessas situações. Grafos são uma poderosa representação estrutural que codifica relações entre objetos e entre seus componentes em um único formalismo. Essa representação é tão poderosa que é aplicada em uma ampla gama de aplicações, de bioinformática a redes sociais. Dessa maneira, diversos problemas de reconhecimento de padrões são modelados para utilizar representações baseadas em grafos. Em problemas de classificação, os relacionamentos presentes entre objetos ou entre seus componentes são explorados para obter soluções efetivas e/ou eficientes. Nesta tese, nós investigamos o uso de grafos em problemas de classificação. Nós propomos duas linhas de pesquisa na tese: 1) uma representação baseada em grafos associados a objetos multi-modais; e 2) uma abordagem baseada em aprendizado para identificar correspondências entre grafos. Inicialmente, nós investigamos o uso do método Sacola de Grafos Visuais para representar regiões na classificação de imagens de sensoriamento remoto, considerando a distribuição espacial de pontos de interesse dentro da imagem. Quando é feita a combinação de representações de cores e textura, nós obtivemos resultados efetivos em duas bases de dados da literatura (Monte Santo e Campinas). Em segundo lugar, nós propomos duas novas extensões do método de Sacola de Grafos para a representação de objetos multi-modais. Ao utilizar essas abordagens, nós combinamos visões complementares de diferentes modalidades (por exemplo, descrições visuais e textuais). Nós validamos o uso dessas abordagens no problema de detecção de enchentes proposto pela iniciativa MediaEval, obtendo 86,9\% de acurácia nos 50 primeiros resultados retornados. Nós abordamos o problema de corresponência de grafos ao propor um arcabouço original para aprender a função de custo no método de distância de edição de grafos. Nós também apresentamos algumas implementações utilizando métodos de reconhecimento em cenário aberto e medidas de redes complexas para caracterizar propriedades locais de grafos. Até onde sabemos, nós fomos os primeiros a tratar o processo de aprendizado de custo como um problema de reconhecimento em cenário aberto e os primeiros a explorar medidas de redes complexas em tais problemas. Nós obtivemos resultados efetivos, que são comparáveis a diversos métodos da literatura em problemas de classificação de grafosAbstract: Many real-world situations can be modeled through objects and their relationships, like the roads connecting cities in a map. Graph is a concept derived from the abstraction of these situations. Graphs are a powerful structural representation, which encodes relationship among objects and among their components into a single formalism. This representation is so powerful that it is applied to a wide range of applications, ranging from bioinformatics to social networks. Thus, several pattern recognition problems are modeled to use graph-based representations. In classification problems, the relationships among objects or among their components are exploited to achieve effective and/or efficient solutions. In this thesis, we investigate the use of graphs in classification problems. Two research venues are followed: 1) proposal of graph-based multimodal object representations; and 2) proposal of learning-based approaches to support graph matching. Firstly, we investigated the use of the recently proposed Bag-of-Visual-Graphs method in the representation of regions in a remote sensing classification problem, considering the spatial distribution of interest points within the image. When we combined color and texture representations, we obtained effective results in two datasets of the literature (Monte Santo and Campinas). Secondly, we proposed two new extensions of the Bag-of-Graphs method to the representation of multimodal objects. By using these approaches, we can combine complementary views of different modalities (e.g., visual and textual descriptions). We validated the use of these approaches in the flooding detection problem proposed by the MediaEval initiative, achieving 86.9\% of accuracy at the Precision@50. We addressed the graph matching problem by proposing an original framework to learn the cost function in a graph edit distance method. We also presented a couple of formulations using open-set recognition methods and complex network measurements to characterize local graph properties. To the best of our knowledge, we were the first to conduct the cost learning process as an open-set recognition problem and to exploit complex network measurements in such problems. We have achieved effective results, which are comparable to several baselines in graph classification problemsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2016/18429-141584/2016-5CAPESFAPESPCNP

An information extraction model for recommending the most applied case

The amount of information produced by different domains is constantly increasing. One domain that particularly produces large amounts of information is the legal domain, where information is mainly used for research purposes. However, too much time is spent by legal researchers on searching for useful information. Information is found by using special search engines or by consulting hard copies of legal literature. The main research question that this study addressed is “What techniques can be incorporated into a model that recommends the most applied case for a field of law?”. The Design Science Research (DSR) methodology was used to address the research objectives. The model developed is the theoretical contribution produced from following the DSR methodology. A case study organisation, called LexisNexis, was to help investigate the real-world problem. The initial investigation into the real-world problem revealed that too much time is spent on searching for the Most Applied Case (MAC) and no formal or automated processes were used. An analysis of an informal process followed by legal researchers enabled the identification of different concepts that could be combined to create a prescriptive model to recommend the MAC. A critical analysis of the literature was conducted to obtain a better understanding of the legal domain and the techniques that can be applied to assist with problems faced in this domain, related to information retrieval and extraction. This resulted in the creation of an IE Model based only on theory. Questionnaires were sent to experts to obtain a further understanding of the legal domain, highlight problems faced, and identify which attributes of a legal case can be used to help recommend the MAC. During the Design and Development activity of the DSR methodology, a prescriptive MAC Model for recommending the MAC was created based on findings from the literature review and questionnaires. The MAC Model consists of processes concerning: Information retrieval (IR); Information extraction (IE); Information storage; and Query-independent ranking. Analysis of IR and IE helped to identify problems experienced when processing text. Furthermore, appropriate techniques and algorithms were identified that can process legal documents and extract specific facts. The extracted facts were then further processed to allow for storage and processing by query-independent ranking algorithms. The processes incorporated into the model were then used to create a proof-of-concept prototype called the IE Prototype. The IE Prototype implements two processes called the IE process and the Database process. The IE process analyses different sections of a legal case to extract specific facts. The Database process then ensures that the extracted facts are stored in a document database for future querying purposes. The IE Prototype was evaluated using the technical risk and efficacy strategy from the Framework for Evaluation of Design Science. Both formative and summative evaluations were conducted. Formative evaluations were conducted to identify functional issues of the prototype whilst summative evaluations made use of real-world legal cases to test the prototype. Multiple experiments were conducted on legal cases, known as source cases, that resulted in facts from the source cases being extracted. For the purpose of the experiments, the term “source case” was used to distinguish between a legal case in its entirety and a legal case’s list of cases referred to. Two types of NoSQL databases were investigated for implementation namely, a graph database and a document database. Setting up the graph database required little time. However, development issues prevented the graph database from being successfully implemented in the proof-of-concept prototype. A document database was successfully implemented as an alternative for the proof-of-concept prototype. Analysis of the source cases used to evaluate the IE Prototype revealed that 96% of the source cases were categorised as being partially extracted. The results also revealed that the IE Prototype was capable of processing large amounts of source cases at a given time